Retail stores or warehouses can have thousands of distinct products that are often sold, removed, added, or repositioned. Even with frequent restocking schedules, products assumed to be in stock may actually be out of stock, decreasing both sales and customer satisfaction. Point of sales data can be used to roughly estimate product availability, but does not help with identifying misplaced, stolen, or damaged products, all of which can reduce product availability. However, manually monitoring product inventory and tracking product position is expensive and time consuming.
One solution for tracking product inventory relies on machine vision technology. Machine vision can be used to assist in shelf space monitoring. For example, large numbers of fixed position cameras can be used throughout a store to monitor aisles, with large gaps in shelf space being flagged. Alternatively, a smaller number of movable cameras can be used to scan a store aisle. Even with such systems, human intervention is usually required to determine product identification number, product count, and to search for misplaced product inventory.